Portfolio

Unsupervised Machine Learning for Airline Frequent Flyer Membership

Implementation of K-means Clustering ML approach in Airline FF membership. From the evaluation, there are 4 cluster. Some actionable insights for marketing team is also present, such as update of membership classification, promotional campaign, and engagement strategy to reach member.

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K-Means Clustering for User Segmentation based on Behavior

Unsupervised Machine Learning Implementation to Website's user behavior data. With the application of K-Means Clustering, a series of group of users are identified which can be used to optimised future marketing campaign in the website.

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Supervised Machine Learning in Revenue Generation based on User’s Web Behavior.

Taking account on user's behavior in a website to predict the critical point of a user's decision making for revenue generation. Based on the approach, a conclusion is taken that Page values is the most affecting aspect with Administrative page as the main source of that value.

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Personal Exploration in Statistics of Marketing Campaign

Working on Marketing Campaign Data. Inclusion of statistical evaluation of tendencies and spread, and hypothesis testing.

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Covid-19 Prevention Analysis with SQL

Analysis on marketing data, including demography, purchase history, campaign performance. The output is a campaign strategy that targetted the customer based on their economical background and purchase behavior

Covid-19 Prevention Analysis with SQL

Based on multinational data on covid-19 spread and prevention, the goal of the project is to have an actionable insight that's implementable for other countries to prevent virus spread. It's found that government action is the most impactful aspect. Initiatives such as regulation. vaccination, hand wash facilities has the most impact toward pressing covid-19 spread

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Regression Model Development in Property Pricing

In Estimating the price of a property, many aspects is needed to be evaluated and considered. Including environment, area, business area, and so on. In this part, an evaluation of variables and impacts towards the property price is done to create a model to predict the pricing of a property.

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Exploratory Data Analysis (EDA) for Telco Customer Churn Data

Projects includes : Data Cleaning, Data Preprocessing, and Analysis (Statistical, Univariate, Multivariate), as well as deeper analysis to see the impact of InternetService and Pricing towards Customer Churn.